code: def process_gap(self, coordinate):
      if any(x > 0 for x in coordinate):
          start = [i for i, x in enumerate(coordinate) if x > 0][0]
          end = [
              i
              for i, x in reversed(list(enumerate(coordinate)))
              if x > 0
          ][0]
          lane = coordinate[start : end + 1]
          # The line segment is not continuous
          if any(x < 0 for x in lane):
              gap_start = [
                  i
                  for i, x in enumerate(lane[:-1])
                  if x > 0 and lane[i + 1] < 0
              ]
              gap_end = [
                  i + 1
                  for i, x in enumerate(lane[:-1])
                  if x < 0 and lane[i + 1] > 0
              ]
              gap_id = [i for i, x in enumerate(lane) if x < 0]
              if len(gap_start) == 0 or len(gap_end) == 0:
                  return coordinate
              for id in gap_id:
                  for i in range(len(gap_start)):
                      if i >= len(gap_end):
                          return coordinate
                      if id > gap_start[i] and id < gap_end[i]:
                          gap_width = float(gap_end[i] - gap_start[i])
                          # line interpolation
                          lane[id] = int(
                              (id - gap_start[i])
                              / gap_width
                              * lane[gap_end[i]]
                              + (gap_end[i] - id)
                              / gap_width
                              * lane[gap_start[i]]
                          )
              if not all(x > 0 for x in lane):
                  print("Gaps still exist!")
              coordinate[start : end + 1] = lane
      return coordinate
  
file_path: PaddleHub/modules/image/semantic_segmentation/bisenet_lane_segmentation/lane_processor/get_lane_coords.py
imports: import cv2
  import numpy
label_desc: Use image node_id 3 for calling the LaneProcessor obj's underlying member method code with example usage: obj.process_gap(coordinate) and returns: coordinate, coordinate, coordinate